AdaBoost

AdaBoost, short for Adaptive Boosting, is a machine learning technique that improves the accuracy of weak classifiers—simple models that perform slightly better than random guessing. It works by sequentially adjusting the focus on misclassified examples, giving them more weight in the next round. Each new model attempts to correct the mistakes of the previous ones. The final prediction combines the strengths of all the models, resulting in a stronger overall classifier. Essentially, AdaBoost intelligently learns from its mistakes, enhancing performance as it builds a robust ensemble of weak learners.